Dataset Information

Imaging of Secreted Extracellular Periostin, an Important Marker of Invasion in the Tumor Microenvironment in Esophageal Cancer.

ABSTRACT:

Unlabelled

Periostin, an extracellular matrix protein, plays key role in cell adhesion and motility within the tumor microenvironment and is correlated with tumor invasion. We developed and characterized a PET tracer that specifically targets periostin and evaluated the probe in preclinical models of esophageal squamous cell carcinoma (ESCC).

Methods

The Institutional Animal Care and Use Committee approved all animal studies. Antiperiostin-F(ab')2 was generated from a monoclonal antibody by enzymatic digestion, conjugated to DOTA, and labeled with (64)Cu. Human ESCC cell lines, TE-11 with high and TT with minimal periostin expression, were implanted in nu/nu mice to generate the positive and control tumor models, respectively. PET/CT imaging was performed at 6, 12, and 24 h and organ-specific biodistribution at 24 h after probe injection. Additionally the probe was tested in a genetically engineered mouse model of periostin-expressing distal esophageal/forestomach ESCC. Tissue microarrays of esophageal neoplasms and ESCC as well as extracted tumor samples were stained for periostin.

Results

We generated a (64)Cu-DOTA-anti-periostin-F(ab')2 with a dissociation constant of 29.2 ± 3.0 nM. PET/CT images and biodistribution studies showed significantly higher tracer uptake in TE-11 than TT tumors (maximum standardized uptake value, 24 h: 0.67 ± 0.09 vs. 0.36 ± 0.03, P < 0.0005; percentage injected dose per gram, 24 h: 3.24 ± 0.65 vs. 1.63 ± 0.49, P < 0.0001). In genetically engineered mouse models, ESCC high periostin tracer uptake anatomically correlated with the (18)F-FDG uptake at the gastroesophageal junction. All of the ESCC cores and 96.2% of adenocarcinoma stained positive for periostin, with most stained strongly (67.3% and 69.3%, respectively).

Conclusion

We demonstrated that specific imaging of extracellular matrix periostin in ESCC is feasible using a targeted PET tracer. Detection of periostin in the tumor microenvironment may help with early detection, postsurgical follow-up, and in situ characterization of primary and metastatic lesions.

SUBMITTER: Heidari P

PROVIDER: S-EPMC4826779 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Imaging of Secreted Extracellular Periostin, an Important Marker of Invasion in the Tumor Microenvironment in Esophageal Cancer.

Heidari Pedram P Esfahani Shadi A SA Turker Nazife S NS Wong Gabrielle G Wang Timothy C TC Rustgi Anil K AK Mahmood Umar U

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20150611 8

<h4>Unlabelled</h4>Periostin, an extracellular matrix protein, plays key role in cell adhesion and motility within the tumor microenvironment and is correlated with tumor invasion. We developed and characterized a PET tracer that specifically targets periostin and evaluated the probe in preclinical models of esophageal squamous cell carcinoma (ESCC).<h4>Methods</h4>The Institutional Animal Care and Use Committee approved all animal studies. Antiperiostin-F(ab')2 was generated from a monoclonal a ...[more]

PMID: 26069303

Similar Datasets

Project description:BackgroundThe abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging.MethodsWe performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. "high" vs "low") prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models' performance was measured via area under the receiver operating characteristic curve (AUC).ResultsUnivariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions.ConclusionsOn two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor's microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed.

Dataset Information

Imaging of Secreted Extracellular Periostin, an Important Marker of Invasion in the Tumor Microenvironment in Esophageal Cancer.

Unlabelled

Methods

Results

Conclusion

Publications

Imaging of Secreted Extracellular Periostin, an Important Marker of Invasion in the Tumor Microenvironment in Esophageal Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets